Acoustics based anomaly detection in machine rooms

ABSTRACT

Monitoring a plurality of machines located in an operating environment. First and second acoustic signal readings and their respective detecting locations are received from a sensing device. First and second acoustic signal spatialization map containing characteristic data signatures for the machines are generated based on the first and second acoustic signal readings. One or more differences are determined that exceed a predetermined threshold value, between corresponding characteristic data signatures in each of the first and second acoustic signal spatialization maps. At least one of the machines that are associated with the determined differences is identified. A corrective action to perform on the machine is identified, based on the determined one or more differences. Commands are transmitted to a corrective action module in the operating environment to cause the corrective action module to perform the corrective action.

BACKGROUND

The present invention relates generally to the field of monitoringsystems, and more particularly to monitoring and determining a conditionof a machine's operation performance.

Various equipment used in many types of buildings are often powered by anumber of machines located in what is often referred to as a machineroom. Depending on factors such as, for example, the size and nature ofuse of the building, the machine room may house many machines. In orderto ensure proper and reliable function and operation, each of themachines may require frequent monitoring and maintenance.

Some machines may include integrated instrumentation and networkcommunications capability, allowing them to self-monitor and identifyconditions relating to their operation performance, such as, forexample, conditions relating to degradation or impending issues, and toreport out such conditions. These machines may execute self-maintenanceor problem-flagging operations as such degradation or impending issuesoccur, allowing for maintenance action to take place before machinefailure can occur. Other machines may not have this capability, and mayrequire certain maintenance practices which might rely on a maintenancetechnician to identify degradation or impending issues in machineoperation performance by detecting human-perceptible indications of suchconditions exhibited by the machine, so as to allow for the subsequentperformance of maintenance.

It is typical for a number of machines to operate within the samemachine room and within close proximity of each other. Such conditionscan contribute to obscuring human-perceptible indications of degradationor impending issues in machine operation performance exhibited by anyparticular machine present in the machine room and can make identifyingsuch indications difficult or impossible. For maintenance practiceswhich might rely on a maintenance technician to identify indications ofdegradation or impending issues in machine operation performanceexhibited by any particular machine, for example, by “listening” to themachines, this may result in such maintenance practices being generallyunreliable, and can ultimately put the proper operation of machines, andconsequently, those people and organizations dependent on such machines,at risk of unplanned downtime, or otherwise.

It would be advantageous to be able to monitor a number of machinespositioned within the same general area as each other in order toidentify degradation or impending issues in a machine's operationperformance, so as to allow for the performance of targeted machinemaintenance well before machine failure can occur.

SUMMARY

Embodiments of the invention are directed to a method, system, andcomputer program product for monitoring a plurality of machines locatedin an operating environment. A first acoustic signal readings and theirrespective detecting locations is received from a sensing device over anetwork to a computing system. A first acoustic signal spatializationmap containing characteristic data signatures is generated by thecomputing system, based on the first acoustic signal readings and theirrespective detecting locations, each of the characteristic datasignatures being associated with one or more of the plurality ofmachines. A second acoustic signal readings and their respectivedetecting locations is received from the sensing device over the networkto the computing system. A second acoustic signal spatialization mapcontaining characteristic data signatures is generated by the computingsystem, based on the second acoustic signal readings and theirrespective detecting locations, each of the characteristic datasignatures being associated with one or more of the plurality ofmachines. One or more differences is determined by the computing systemthat exceeds a predetermined threshold value, between one or morecharacteristic data signatures in the first acoustic signalspatialization map and corresponding one or more characteristic datasignatures in the second acoustic signal spatialization map. At leastone of the plurality of machines that are associated with the determineddifferences is identified. A corrective action to perform on a machineof the plurality of machines is identified by the computing system,based on the determined one or more differences in the generated firstand second acoustic signal spatialization maps. Commands are transmittedby the computing system to a corrective action module in the operatingenvironment to cause the corrective action module to perform theidentified corrective action.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram depicting a machine room environment, inaccordance with an embodiment of the present invention.

FIG. 2 is a functional block diagram depicting a machine operationmonitoring system, in accordance with an embodiment of the presentinvention.

FIG. 3 is a functional block diagram depicting a data characterizationand correlation module of FIG. 2, in accordance with an embodiment ofthe present invention.

FIG. 4 is a flowchart depicting the operational steps of an aspect ofthe machine operation monitor program of FIG. 2, in accordance with anembodiment of the present invention.

FIG. 5 is a block diagram depicting a sensing device, in accordance withan embodiment of the present invention.

FIG. 6 is a block diagram depicting a computing device, in accordancewith an embodiment of the present invention.

FIG. 7 depicts a cloud computing environment, in accordance with anembodiment of the present invention.

FIG. 8 depicts abstraction model layers, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

For certain types of machines, indications of degradation or impendingissues in machine operation performance may occur as machine-emittedacoustic or electromagnetic signals.

Embodiments of the present invention are directed to a monitoring systemthat monitors machines in a machine room, and which can detect anddetermine a condition of a machine's operation performance on the basisof machine-emitted acoustic or electromagnetic signals, in order toperform targeted corrective action. In the disclosed embodiments of thepresent invention, the monitoring system operates in an enclosedenvironment in which a number of distinct machine-emitted signalsources, as well as other distinct signal sources, may each emitsignals, either continuously, periodically, or intermittently. Aninitial reading of the machine-emitted signals, indicative of a currentcondition of each of the machines' operation performance, is received,characterized, spatialized and clustered with respect to each of themachines, in order to generate, associate, and index an initialcharacteristic data signature, in the form of either of a characteristicacoustic signature, or a characteristic electromagnetic signature, toeach of the machines, respectively. During machine monitoring, a laterreading of the machine-emitted signals is taken, which is alsocharacterized, spatialized, and clustered. Differences in each of amachine's initial and later characteristic data signatures are used as abasis to detect differences in each of the machine's current and laterconditions as to its operation performance. Where differences in thecharacteristic data signatures exceed a predetermined threshold value,indicating a possible degradation or impending issues in the machine'soperation performance, corrective action on the machine may beperformed.

FIG. 1 is a schematic diagram depicting a machine room environment 100,in accordance with an embodiment of the present invention. Machine roomenvironment 100 is defined by the three dimensional space enclosed bythe boundaries and walls of machine room enclosure 101, which mayinclude an access door 104. Inside machine room enclosure 101 there maybe positioned an air handling unit 110, which may include air handler120 along with associated distribution ducting 122, a water control anddistribution unit 170, which may include water heaters 180A-B along withassociated distribution piping 186A-F, fluid flow control elements182A-D, and fluid conveyance elements 184A-B. Machines 102A-H may alsobe positioned in the machine room enclosure 101. Data collection points190, as will be described in further detail below, represent positionsin the machine room enclosure 101 from which a sensing unit mightcollect machine-emitted acoustic signals. CAMs 130A-B, as will bedescribed in further detail below, represent means for performingcorrective actions on machines 102A-H.

In an exemplary embodiment of the present invention, machines 102 andcertain components of air handling unit 110 and water control anddistribution unit 170 represent, for example, machinery, with rotatingand/or reciprocating components, used in the operation of a building'spower and utility systems. These types of systems might include, forexample, power storage or generation systems, cooling and heatingsystems, or other types of systems, and might involve the use ofmachines such as, for example, inertial flywheels, transformers,generators, dynamos, alternators, prime movers such as diesel orgasoline engines, motors, turbines, and fluid conveyors and regulatorssuch as pumps, compressors, fans, and valves.

Rotating or reciprocating machines typically produce vibrations andemitted acoustic signals during their operation due to their productionof dynamic forces that vary with respect to time, which act bothinternally within the machines, and externally upon the machines'surrounding environment. These internally and externally acting forcesmay be affected by factors such as, for example, those relating to anoperating environment of the machine, the machine's overall design, thematerials used in the machine's construction, and the dampingcharacteristics of the materials used, and may vary from machine tomachine. Rotating or reciprocating machines produce vibrations andemitted acoustic signals, which, with continued operation and use, maychange in terms of character over time due to, for example, degradationof a machine in the form of “wear and tear” of certain of the machine'scomponents. For these types of machines, observations of thecharacteristics of the machine's emitted acoustic signals, and thedetection of any changes relating to the machine's emitted acousticsignals over time, can be used as a basis for monitoring and determininga condition of the machine's operation performance.

As the condition of the machine's operation performance begins toundergo some degree of degradation, or if the machine fails suddenly,the machine's produced vibrations and emitted acoustic signals may be ofa certain character. For example, bearing wear or a loosened powertransmission belt of a machine may affect a condition of the machine'soperation performance by increasing friction or misalignment betweencomponents of the machine, and may cause the machine to undergo somedegree of degradation. This degradation may change the character of themachine's produced vibrations and emitted acoustic signals, causing themachine to produce emitted acoustic signals that weren't present before,in the form of, for example, “screeching”, “scraping”, or “knocking”sounds.

Generally, acoustic signals emitted from various types of machinespresent in a machine room, as perceived from a position located withinthe machine room, might range in magnitude from approximately 40-130 dBSPL, and more particularly from approximately 50-105 dB SPL. Dependingon the particular types of machines and the machines' nature of use,frequencies of machine-emitted acoustic signals may range fromapproximately 20 Hz-24 kHz.

In an exemplary embodiment of the present invention, the characteristicsof machine-emitted acoustic signals in and of machine room enclosure101, as perceived from a particular position inside machine roomenclosure 101, may be affected by, for example, the operation andpositioning of any of machines 102, certain components of air handlingunit 110, and certain components of water control and distribution unit170, as well as the physical boundaries of machine room enclosure 101.The machines may operate continuously, periodically, or intermittentlydue to reasons relating to, for example, demands for electrical power,climate and temperature control, or to supply and regulate the flow ofutilities. For example, the operation of a generator might becontinuous, or only occur during times when electrical power from apower grid is insufficient for a user's needs, and additional power isrequired. Fluid flow through distribution ducting 122 or distributionpiping 186 may become turbulent as a result of sudden changes in fluidpressure caused by the opening, closing, or throttling of valves orother fluid flow regulators such as fluid flow control elements 182, orthe starting and stopping of operation of one or more pumps such as anyof fluid conveyance elements 184, where such turbulent fluid flowthrough distribution ducting 122 or distribution piping 186 might causethem to vibrate and emit acoustic signals.

The boundaries and walls of machine room enclosure 101, including accessdoor 104, as well as the machines and other structures or objects thatmay be present, act as physical objects with which the machine-emittedacoustic signals may impinge, altering the characteristics of suchsignals by causing various levels of acoustic signal diffraction,reflection, absorption, and dissipation to occur as a function of timeand space. This can have the effect, among others, of introducing phasechanges to, or otherwise changing the characteristics of, the signals.

In various embodiments of the present invention, machines 102 representmachinery used in the operation of one or more of a manufacturingfacility's manufacturing or fabrication systems, such as, for example, aplant floor, or a machine shop. These types of systems might include,for example, subtractive or additive manufacturing systems, and othertypes of systems, and might involve the use of machines, such as, forexample, drills, lathes, polishers, sanders, augers, pick-and-placerobots, prime movers such as motors, fluid conveyors and regulators suchas pumps, compressors, and valves, vacuum deposition and other types ofmaterial deposition machines, wet- or dry-cleaning machines, 3Dprinters, and other types of injection, extrusion, or die mouldingmachines. These types of machines may operate continuously,periodically, or intermittently due to reasons relating to, for example,production demands or standard product workflow.

FIG. 2 is a functional block diagram depicting a machine operationmonitoring system 200, in accordance with an embodiment of the presentinvention. Machine operation monitoring system 200 includes machines102G-H, sensing unit 220, CAMs 130A-B, and server 230, allinterconnected over a network 210.

In various embodiments of the present invention, network 210 can be, forexample, an intranet, a local area network (LAN), a wide area network(WAN) such as the Internet, and include wired, wireless, or fiber opticconnections. In general, network 210 can be any combination ofconnections and protocols that will support communications betweensensing unit 220, each of machines 102G-H, and server 230, in accordancewith an embodiment of the invention.

Server 230 represents a computing platform that hosts machine operationmonitor program 240. Server 230 may include internal and externalhardware components as depicted and described in further detail belowwith reference to FIG. 6, below. In other embodiments, the computingsystem may be implemented in a cloud computing environment, as describedin relation to FIGS. 7 and 8, below.

In an exemplary embodiment of the present invention, machines 102G-Hrepresent, for example, machines which may include integratedinstrumentation and network communications capability, as well ascomputing resources, allowing machines 102G-H to self-monitor andidentify conditions relating to their operation performance, and toprocess and communicate data relating to such conditions over a networkto a centralized control system so as to allow for a determination to bemade as to any required corrective action that should be performed. Theself-monitoring and maintenance capability of each of machines 102G-Hmay use control systems, such as, for example, modularized controlsystems, which may include, for example, integrated components orcontrollers of a wired or wireless, network-connected, supervisorycontrol and data acquisition (SCADA) system, or by a distributed controlsystem (DCS).

Regardless of the machines' integrated instrumentation and networkcommunications capability, all machines 102A-H may be monitored bysensing unit 220 of machine operation monitoring system 200. Formachines such as machines 102G-H, which may already have self-monitoringand maintenance capability, monitoring by sensing unit 220 of machineoperation monitoring system 200 may provide redundancies in themonitoring of these types of machines, and may enhance their operationalreliability, by functioning as an auxiliary monitoring system.

In an exemplary embodiment, sensing unit 220 represents a device whichdetects or takes readings of acoustic signals. The sensing unit 220includes a sensor to receive and sample, or otherwise allow for thesensing unit 220 to take readings of acoustic signals. Generally, thesensor may take on any form, including, for example, a capacitive form,a microelectrical mechanical system form, a piezoelectric form, a fiberoptic form, and the like, and may enable measurement of a physicalquantity or form of acoustic energy in the environment of the device,such as, for example, one or more mechanical time-varying propagatingacoustic waves. The sensor may produce data and informationrepresentative of the physical quantity or form of energy measured. Thesensing unit 220 may include internal and external hardware componentsas depicted and described in further detail below with reference to FIG.5, allowing it to detect acoustic energy, to produce, for example, ananalog electrical signal according to the detected acoustic energy, andto process the analog signal to generate digital data packets otherwiseknown as network packets, packets, or units of data. The sensing unit220 may include network communications capability, allowing it totransmit the generated data packets over a network to a computing unit.

Generally, data packets generated by sensing unit 220 may includemetadata. For example, the metadata might include time and dateinformation relating to particular detected acoustic signals, andinformation concerning the spatial position and orientation of thesensing unit 220, as will be described in further detail below.

In various embodiments of the present invention, sensing unit 220 mayrepresent a device, such as, for example, a sound level meter, a noisedosimeter, or any other device capable of detecting acoustic signals.The sensing unit 220 may include an acoustic sensor, such as, forexample, a microphone. Generally, the sampling rate and dynamic rangecharacteristics of the sensing unit 220 may be chosen to accommodate thetypes of machines 102A-H to be monitored, according to the character ofthe machine-emitted signals emitted by each of the machines 102A-H. Forexample, the sampling rate and dynamic range characteristics of thesensing unit 220 may be chosen so as to allow for sampling of receivedsignals at sampling rates of, for example, between approximately 20Hz-48 kHz, for acoustic signals ranging in magnitude, for example,between approximately 40 db SPL-130 db SPL.

In an exemplary embodiment of the present invention, sensing unit 220represents a device, such as, for example, a mobile phone or smartphone, for example, a mobile phone as described in connection with FIG.5. The sensing unit 220 may include numerous sensors including, forexample, microphone 842, electromagnetic interference (EMI) sensor 844,camera 846, and other sensing units 848. Generally, the sensing unit 220may include an acoustic sensor, such as, for example, microphone 842,which may allow the sensing unit 220 to receive and sample, or otherwiseallow for the sensing unit 220 to take readings of acoustic signals. Thesensing unit 220 may be, for example, an IPHONE®, manufactured and soldby APPLE®, Inc. of Cupertino, Calif. The sampling rate and dynamic rangecharacteristics of the sensing unit 220 may generally be chosen toaccommodate the types of machines to be monitored according to thecharacter of their machine-emitted signals, as previously described.

Data collection points 190, as depicted in FIG. 1, represent positionswithin machine room enclosure 101 at which sensing unit 220 may takereadings of acoustic signals, for use in generating acoustic signalspatialization maps, or representations of an operating environment suchas machine room enclosure 101. Generally, acoustic signal spatializationmaps may contain information relating to the characteristics of acousticsignals emitted by each of the machines being monitored and otheracoustic signal sources, information relating to the physical locationsof each of the machines being monitored, and information relating to thephysical locations of the other acoustic signal sources, as will bedescribed in further detail below.

Although FIG. 1 is a two-dimensional plan view of machine room enclosure101, it should be understood that data collection points 190 are usuallydistributed within operating environments defined by three-dimensionalspace. Spatial positioning and orientation of the sensing unit at anumber of different positions may have the effect of reducing the impactthat signal interference may have on generated acoustic signalspatialization maps of machine room enclosure 101, and on monitoring ofmachines present in machine room enclosure 101.

In various embodiments, sensing unit 220 may, for example, take acousticreadings at data collection points 190 for 0.5 to 1 second. The datacollection points 190 may advantageously be positioned so as to allowfor the sensing unit 220 to receive machine-emitted acoustic signalsfrom each of the machines being monitored. For example, for athree-dimensional space such as the space defined by machine roomenclosure 101, the sensing unit 220 may be positioned at increasingdistances from some reference point such as access door 104, atequally-spaced, in-line positions, spanning the length, width, andheight of the space, where the number of the positions may be dividedequally amongst lines of positions that project into the space at evenlydistributed angles with respect to the horizontal and vertical.

In various embodiments of the present invention, a determination as toboth positioning, and a number of positions at which sensing unit 220takes acoustic signal readings may be determined heuristically. Forexample, by observing the relationship between accuracies ofrepresentation by a number of generated acoustic signal spatializationmaps, and the respective positioning and number of positions at whichacoustic signal readings were taken.

In an exemplary embodiment of the present invention, acoustic signalreadings by sensing unit 220 may be performed initially to produce ahigh accuracy spatialization map. In an exemplary embodiment, between 6and 60 acoustic signal readings may be taken by sensing unit 220 forthis initial phase. In later monitoring phases, fewer acoustic readingsmay be taken, for example, as few as two. However, as described in moredetail below, taking fewer acoustic readings may produce a less accuratespatialization map. Generally, the data collection points 190 at whichsensing unit 220 takes acoustic signal readings should allow for thereceived acoustic signals to include machine-emitted acoustic signalsfrom each of the monitored machines.

In an exemplary embodiment of the present invention, spatial positioningand orientation of sensing unit 220 may be controlled by mounting thesensing unit 220 to a mobile platform. The mobile platform may beautonomous, and controlled, for example, by an onboard or remote controlsystem. For autonomous mobile platforms, control may be effected, forexample, by way of a controller and one or more sensors, such as, forexample, sensor motes, of the autonomous mobile platform. The controllermay be incorporated into sensing unit 220, or may be a commercialoff-the-shelf controller that may be designed to operate in conjunctionwith the mobile platform. The controller may enable robotic mapping andnavigation of machine room enclosure 101, by way of computationalalgorithms such as, for example, simultaneous localization and mapping(SLAM) algorithms. This machine room mapping may enable the mobileplatform to avoid obstacles when moving within machine room enclosure101, when given commands to travel from one position to another, forexample, to allow for the sensing unit 220 to receive signals atdifferent points in space. An example of an implementation of such acontrol system is the ROOMBA® vacuum cleaner, manufactured and sold byIROBOT®, of Bedford, Mass., U.S.A.

The mobile platform may take on various forms, such as, for example, aland-based wheeled or limbed vehicle, or an aerial vehicle or “drone”.The mobile platform may also take on the form of a robotic arm, whichmay be attached to, for example, a wall, floor, or ceiling, or to agantry or railing structure, of machine room enclosure 101. In variousembodiments of the present invention, the mobile platform may take on aform which may be some combination of the embodiments described above.

In various embodiments of the present invention, spatial positioning andorientation of sensing unit 220 may be performed by way of manualpositioning by, for example, a maintenance technician. For example, themaintenance technician may position the sensing unit 220 with his or herhand, or attach the sensing unit 220 to, for example, a boom, rod, orother apparatus, for handling and positioning.

Corrective action modules (CAMs) 130A-B represent devices which performcorrective action on monitored machines. CAMs 130A-B may includeinternal and external hardware components, as well as networkcommunications components, as depicted and described in further detailbelow with reference to FIG. 5, allowing them to receive commands fromserver 230, as will be described in further detail below. CAMs 130A-Bmay include one or more onboard or remote control systems, as previouslydescribed in connection with autonomous spatial positioning andorientation of sensing unit 220, allowing them to execute and performcorrective actions according to received commands, as will be describedin further detail below. CAM 130A may include navigation capability,allowing for selective mobile positioning of CAM 130A according toreceived commands. CAMs 130A-B may include one or more rotatably and/orpivotally attached, cantilevered robotic arms with a number of degreesof freedom, usable to perform corrective action. The one or more roboticarms of the devices may include one or more end effectors, or componentsusable for grasping, gripping, or otherwise manipulating a machine part,and may also include, for example, one or more electrical orelectromechanical devices usable for interfacing, or otherwiseinteracting, with a machine, such as, for example, by way ofradio-frequency communication. Generally, means to perform correctiveaction by CAMs 130A-B may take on any form that might allow for physicalor other interaction with monitored machines.

In an exemplary embodiment of the present invention, CAM 130A representsa mobile device. An example of the mobile device is the KUKA® MobileRobot intelligent industrial work assistant (KMR iiwa) mobile robot,manufactured and sold by KUKA® Robotics Corporation of Augsburg,Bavaria, Germany. As depicted, CAM 130B represents a device which may,for example, be rotatably and/or pivotally attached to a machine, suchas, for example, machine 102A, as depicted in FIG. 1. An example of theCAM 130B is the lightweight robot intelligent industrial work assistant(LBR iiwa robot), manufactured and sold by KUKA® Robotics Corporation ofAugsburg, Bavaria, Germany.

Corrective action performed by CAMs 130A-B may take the form of, forexample, lubrication of a machine's bearings or other parts, fasteningor replacement of loosened connections between parts or portions of themachine, varying of the machine's operating parameters, such as, forexample, varying a drive motor output speed of the machine, or shuttingthe machine down. Other forms of corrective action might involve, forexample, electrical or radio-frequency communication with the machine orcomponents of the machine, so as to vary or control, for example,factors relating to any of the machine's operational parameters. Suchcorrective action might involve, for example, lubricating one or morecomponents of the machine by opening and closing a valve of the machine,or varying a drive motor output speed of the machine. CAMs 130A-B mayinclude trouble ticketing systems or software database logging systems,which may be communicated between each of CAMs 130A-B, in which acorrective action to be performed may be noted and stored, in order toallow for later performance of the corrective action. For example, CAMs130A-B may note and store corrective actions to be performed at a latertime, when commands regarding the corrective actions are received at atime when CAMs 130A-B are occupied with the performance of othercorrective actions. Generally, a corrective action may involve anyaction or sequence of actions that affect a machine's operationperformance, with respect to, for example, maximizing efficiency orreliability, or minimizing unscheduled downtime.

Machine operation monitor program 240, residing on server 230,represents a computer program which receives and processes datagenerated by sensing unit 220 to determine conditions as to operationperformance of each machine being monitored, and to determine andgenerate commands to perform corrective action on any of the machinesbeing monitored, accordingly. Machine operation monitor program 240includes data collection module 242, data characterization andcorrelation module 244, data anomaly detection module 246, correctiveaction module 248, and data storage 250.

Data collection module 242 receives acoustic signal reading data fromsensing unit 220, for example, in the form of data packets received fromsensing unit 220 over network 210, and stores each acoustic reading indata storage 250 in the form of, for example, separate data files.Generally, each acoustic reading data file contains signal data, as wellas corresponding metadata, as previously described. For example, datacollection module 242 may receive and store sixty individual acousticreadings, corresponding to sixty readings taken by the sensing unit 220at sixty distinct data collection points 190 and orientations withinmachine room enclosure 101.

Data characterization and correlation module 244 receives sets ofacoustic reading data files for an initial phase and a later monitoringphase, and, for each set of acoustic reading data files, generates anacoustic signal spatialization map which includes characteristic datasignatures for each monitored machine 102, and an associated location ofeach machine in machine room enclosure 101.

FIG. 3 is a functional block diagram depicting the data characterizationand correlation module 244 of FIG. 2, in accordance with an embodimentof the present invention. Data characterization and correlation module244 includes data characterization program 302, signature formulationprogram 304, signature to source location mapping program 306, andsignature to machine identity mapping program 308.

Data characterization program 302 receives sets of acoustic reading datafiles, each data file containing signal data and metadata, as previouslydescribed, and performs digital signal processing on the signal data andmetadata contained in each acoustic reading data file to producecharacterized signal data. Data characterization program 302 may performvarious types of computations in order to characterize the signal datain terms of one or more time- and/or frequency-domain characteristics ofthe signal data, or temporal and/or spectral features of the signaldata, respectively. Signal data may also be characterized with the time-and/or frequency-domain characteristics, in terms of one or moretime-frequency domain characteristics, and/or in terms of one or morestatistical measurements. The statistical measurements may be based onthe signal data, or based on any of the time- and/or frequency-domaincharacteristics, or time-frequency domain characteristics of the signaldata.

The computations performed by data characterization program 302 may useone or more algorithms, either individually or in combination, tocompute and produce time-domain characteristics, frequency-domaincharacteristics, time-frequency domain characteristics, and/orstatistical measurements, with respect to signal data, representative ofacoustic or electromagnetic signals. The algorithms may consist of, orinvolve the use of, various types of transforms and other types ofcalculations, which may include, for example, either individually or incombination, any of the Laplace transform, Fourier transform (FT), fastFourier Transform (FFT), discrete Fourier transform (DFT), discrete sinetransform (DST), discrete cosine transform (DCT), discrete wavelettransform (DWT), Mel-frequency cepstrum (MFC), Mel-frequency cepstralcoefficients (MFCCs), linear prediction cepstral coefficients (LPCCs),and/or analog-to-information (ATI). Other computations, eitherindividually or in combination, may involve the use of sparseapproximation algorithms, such as, for example, matching pursuit (MP),basis pursuit (BP), or orthogonal matching pursuit (OMP) algorithms ortheir variants. Other computations, either individually or incombination, may involve the use of algorithms to compute the short-timeenergy, short-time average zero-crossing rate, and/or time-domainharmonics amplitudes (TDHA) of the signal data. Other computations,either individually or in combination, may involve the use of algorithmsto compute the signal energy, mean, variance, skewness, kurtosis, andthe like, of the signal data. Generally, any types of algorithms may beimplemented that may allow for the representation of the signal data interms of a number of time-domain characteristics, frequency-domaincharacteristics, time-frequency domain characteristics, and/orstatistical measurements, where the choice of any particular algorithmor group of algorithms to be used may be a matter of design choice.

Signature formulation program 304 receives acoustic reading data filescontaining characterized signal data from data characterization program302, and performs digital signal processing on the characterized signaldata in order to generate, associate, and index individualcharacteristic data signatures contained in each acoustic reading datafile. Generally, the signal data includes contributions from a number ofdistinct acoustic signals, emitted from a variety of distinct acousticsignal sources. As a result, there may be a number of time-domaincharacteristics, frequency-domain characteristics, and/or time-frequencydomain characteristics of the characterized signal data that may be usedby signature formulation program 304 to define and generate eachcharacteristic data signature. The appropriate choice of these featuresmay be advantageous in building a robust recognition system.

The computations performed by signature formulation program 304 may useone or more algorithms, either individually or in combination, toclassify characterized signal data in terms of one or more producedtime-domain characteristics, frequency-domain characteristics,time-frequency domain characteristics, and/or statistical measurementswith respect to characterized signal data. Classified characterizedsignal data may form the bases for the definition and generation of eachcharacteristic data signature, so as to allow for the identification anddistinction of each individual machine-emitted signal represented by thecharacterized signal data. The algorithms may consist of, or involve theuse of, various types of signal classification algorithms, feedforwardneural networks, and/or other types of classifiers or estimationtechniques, which may include, for example, either individually or incombination, maximum likelihood estimation (MLE), maximum a posterioriprobability (MAP) estimation, harmonic signal classifiers, and/orclassifiers based on a modified version of the MP decompositionalgorithm. Generally, any types of algorithms may be implemented thatmay allow for the classification of characterized signal data in termsof one or more produced time-domain characteristics, frequency-domaincharacteristics, time-frequency domain characteristics, and/orstatistical measurements with respect to characterized signal data,where the choice of any particular algorithm or group of algorithms maybe a matter of design choice.

Signature to source location mapping program 306 spatializescharacteristic data signatures, and may operate in parallel withsignature formulation program 304. Spatialization may involve performingsound localization computations using the signal data and metadataassociated with each characteristic data signature belonging to a set ofcharacteristic data signatures, generated according to a respective setof acoustic signal readings taken by the sensing unit 220, for example,as in the set of acoustic signal readings used to generate an initialacoustic signal spatialization map, as previously described. Soundlocalization computations may involve detecting differences in thesignal data and metadata associated with individual acoustic signalreadings by the sensing unit 220 at distinct positions and orientations,in terms of, for example, differences in the magnitude of the acousticsignal readings, and the corresponding position and orientation at whichthe signals were detected by the sensing unit 220. The soundlocalization computations may involve the use of one or more algorithms,such as, for example, algorithms which implement an adapted form of ageneralized cross-correlation, which may use detected differences in themagnitude of detected signals and corresponding positions andorientations of detection of the signals, to localize acoustic signalsources.

Signature to machine identity mapping program 308 receives spatializedcharacteristic data signatures and associates each of the spatializedcharacteristic data signatures with a unique identity or label,respectively. Generally, each spatialized characteristic data signaturecorresponding to a location of a machine to be monitored may beassociated with a unique identity in the form of, for example, a machineidentifier, such as, for example, a serial number of the machine knownto be operating at the location coinciding with the location of thespatialized characteristic data signature. For example, informationrelating to a serial number of a machine, as well as the machine'sparticular location, may be referenced from a database, or provided byway of user input, according to the corresponding location of aspatialized characteristic data signature, and assigned accordingly foreach spatialized characteristic data signature. Signature to machineidentity mapping program 308 may additionally associate with eachspatialized characteristic data signature information relating tolocations of signal sources corresponding to each spatializedcharacteristic data signature, respectively.

Spatialized characteristic data signatures may collectively formacoustic signal spatialization maps, as previously described.Spatialized characteristic data signatures may be represented byacoustic signal spatialization maps in the form of one or more arrays ormatrices which may include, for example, numerical values representativeof each spatialized characteristic data signature, respectively, whereeach numerical values may populate the rows and columns of the matricesor arrays.

Returning to FIG. 2, data anomaly detection module 246 receives theacoustic signal spatialization maps generated by signature to machineidentity mapping program 308.

Data anomaly detection module 246 receives and compares acoustic signalspatialization maps, generated based on sets of acoustic reading datafiles, for example, acoustic reading data files for an initial phase anda later monitoring phase, as previously described, to detect differencesbetween corresponding characteristic data signatures contained in eachof the acoustic signal spatialization maps, respectively, which exceed apredetermined threshold value. Data anomaly detection module 246 maydetect differences in corresponding characteristic data signatures bycomparing corresponding pairs of acoustic signal spatialization maps,and computing differences in numerical values, as previously described,representative of the corresponding characteristic data signatures.

Where an initial phase acoustic signal spatialization map is based on agreater number of acoustic reading data files, for example, sixtyacoustic reading data files, and a corresponding monitoring phaseacoustic signal spatialization map is based on a fewer number ofacoustic reading data files, for example, two acoustic reading datafiles, a detected data anomaly may not be accurately spatialized to aparticular machine in the initial phase acoustic signal spatializationmap. Rather, data anomaly detection module 246 may spatialize the dataanomaly to a group of machines in a larger area in the acoustic signalspatialization map. For example, where a machine, for example, machine102G, as depicted in FIG. 1, may be the actual source of a detected dataanomaly, data anomaly detection module 246 may spatialize the dataanomaly to two or more machines, for example, machines 102D-G. It may,however, be more time-effective to take a few acoustic readings, forexample, two or three, during a monitoring phase to determine if ananomaly exists, and if so, take additional readings for the area ofmachine room enclosure 101 in which the anomaly was spatialized in orderto get a more accurate spatialization to a particular machine 102.

In an exemplary embodiment of the present invention, corrective actionmodule 248 represents machine-readable program instructions of machineoperation monitor program 240 that receives output from data anomalydetection module 246 and determines or identifies an appropriatecorrective action to be performed on a monitored machine based on thereceived output. A particular corrective action to be performed maydepend on the extent and nature of differences between sets of currentand corresponding later characteristic data signatures, as previouslydescribed. For example, machine-emitted acoustic signals indicative of awearing bearing, as identified by data anomaly detection module 246, maycause corrective action module 248 to determine and communicate anappropriate corrective action to perform on the machine to either ofCAMs 130A-B, where such corrective action might involve, for example,lubrication of the wearing bearing, or deactivation of the machine, byeither of CAMs 130A-B.

Data storage 250 may operate to store all data regarding characterizeddata signatures, as well as associated data and other related data, forretrieval and use by machine operation monitor program 240 and any ofits associated modules.

FIG. 4 is a flowchart illustrating the operational steps of an aspect ofmachine operation monitoring system 200 of FIG. 2, in accordance with anembodiment of the present invention.

Data collection module 242 of machine operation monitor program 240,residing on server 230, receives signal data and metadata over network210 from sensing unit 220, in the form of, for example, data packets(step 402). Data collection module 242 may store received data packetsin the form of separate computer-readable files on data storage 250 forlater retrieval.

Data characterization program 302, of data characterization andcorrelation module 244, receives signal data and metadata from datacollection module 242, and generates characterized signal dataaccordingly, in terms of a number of temporal and/or spectral featuresof the signal data, in order to identify and distinguish between eachdistinct acoustic signal composing the received signal data (step 404).Data characterization and correlation module 244 may further associateand index such temporal and spectral features with the producedcharacterized signal data.

Signature formulation program 304 receives characterized signal data,and defines and generates characteristic data signatures according to anumber of the produced temporal and spectral features of thecharacterized signal data, by using certain temporal and/or spectralfeatures of the received characterized signal data (step 406). Signatureformulation program 304 may associate and index the selected temporaland/or spectral features to the defined characteristic data signaturesfor further processing.

Signature to source location mapping program 306 spatializes eachcharacteristic data signature by using signal data and metadataassociated with each of the characteristic data signatures, to determinephysical locations of acoustic signal sources corresponding to each ofthe characteristic data signatures (step 408). Signature to sourcelocation mapping program 306 may associate, cluster, and index suchspatialization data to the characterized data signature accordingly.Signature to machine identity mapping program 308 may associatespatialized characteristic data signatures with particular machineidentities.

Data anomaly detection module 246 receives and compares first definedcharacteristic data signatures with corresponding, second definedcharacteristic data signatures, in the form of corresponding acousticsignal spatialization maps, as received from data characterization andcorrelation module 244 (step 410). Data anomaly detection module 246detects differences between characteristic data signatures on the basesof one or more temporal and/or spectral features of the correspondingcharacteristic data signatures that exceed a predetermined threshold(step 412). For compared characteristic data signatures that do notdiffer beyond a predetermined threshold value, no corrective action maybe performed.

Corrective action module 248 receives detected differences betweencharacteristic data signatures that exceed a predetermined threshold,and generates and communicates commands to, for example, either of CAMs130A-B, to execute and perform corrective action on a machine of themonitored machines 102A-H, accordingly (step 414), with respect toconditions as to operation performance of the machine.

In alternative embodiments of the present invention, machines 102, asdepicted in FIG. 1, may be machines which emit detectableelectromagnetic radiation or signals (EMF) which may be characterized togenerate corresponding characterized data signatures, as previouslydescribed. Similar to above, these characterized data signatures can beused to generate EMF signal spatialization maps. The machines may be,for example, servers, network routers, network switches, and the like,which may operate, for example, in a data processing environment, suchas a computer room of a data center. In another example, the machinesmay be recording boards, mixing consoles, sound amplifiers, recordingdevices, etc., which may operate, for example, as a recording studio.The machines may include electrical components, such as, for example,integrated circuits, printed circuit boards, and the like, and mayfurther include various types of active electrical component such as,for example, transistors, resistors, diodes, and the like.

Generally, these types of machines may emit electromagnetic radiation orsignals due to the nature of their operation. The nature of emittedelectromagnetic signals from these types of machines may be affected byfactors such as, for example, those relating to an operating environmentof a machine, the machine's overall design, the materials used in themachine's construction, and may vary from machine to machine. Thesetypes of machines may produce and emit electromagnetic signals which,with continued operation and use, may change in terms of character overtime due to, for example, degradation of a machine in the form of “wearand tear” of certain of the machine's components, such as, for example,one or more electrical components of the machine, as previouslydescribed. For these types of machines, the detection andcharacterization of machine-emitted electromagnetic signals, and anychanges relating to the machine-emitted electromagnetic signals overtime, can be used as a basis for monitoring and determining conditionsof a machine's operation performance.

In these embodiments, the sensing unit 220 represents a device whichdetects electromagnetic signals. The sensing unit 220 may represent adevice such as, for example, an electromagnetic radiation meter, whichmay include, for example, one or more antennae, which may be integratedinto EMI sensor 844, allowing the sensing unit 220 to receive andsample, or otherwise allow for the sensing unit 220 to take readings ofelectromagnetic signals. Generally, the one or more antennae may be anyelectrical device which receives and converts one or moreelectromagnetic signals into electrical power, which may be amplifiedand measured by the sensing unit 220. The sensing unit 220 may includeinternal and external hardware components as depicted and described infurther detail below with reference to FIG. 5, allowing it to detectelectromagnetic energy, and to produce, for example, an analogelectrical signal according to the detected electromagnetic energy, andto process the analog signal to generate digital data packets. Thesensing unit 220 may include network communications capability, allowingit to transmit the generated data packets over a network, such asnetwork 210, to a computing unit, such as server 230. The sensing unit220 may be capable of detecting electromagnetic signals in the range of,for example, 100 kHz-100 GHz. The sensing unit 220 may be, for example,a Nardalert S3 Mainframe ultra-wideband electromagnetic radiationmonitor, manufactured and sold by Narda Safety Test Solutions GmbH, asubsidiary of L3 Communications Holdings, of New York City, N.Y.

Data packets generated by sensing unit 220 may be received by machineoperation monitor program 240, residing on server 230, in order to allowfor the determination and generation of commands relating to correctiveaction to be performed, as previously described. In these embodiments,the performance of corrective action may take the form of commandsexecutable by a machine in order to shut down the machine, generated andsent by machine operation monitor program 240 accordingly.

In other alternative embodiments of the present invention, machines 102may operate while partially or completely submerged in a liquid, and maybe used in various types of excavation, mining, mineral retrieval,mineral processing systems, and the like. The machines might include, oroperate as part of, for example, oil or offshore platforms, heatexchanging machines, hydrocarbon extraction machines, clarifiers,flotation machines, machines used in hydraulic fracturing, and the like.

In these embodiments, the sensing unit 220 may represent a device whichdetects acoustic signals, as previously described, with the exceptionthat the sensing unit may further be adapted for use while submerged ina liquid, such as water. The sensing unit 220 may be, for example, a100-800 kHz differential underwater acoustic emissions sensor,manufactured and sold by PHYSICAL ACOUSTICS®, of Princeton Junction,N.J.

Spatial positioning and orientation of sensing unit 220 may be performedby way of mounting to a mobile platform, in the form of a completely orpartially submersible, or buoyant, marine vehicle. The mobile platformmay be autonomous, and controlled, for example, by an onboard or remotecontrol system, as previously described.

As depicted in FIG. 5, mobile phone SU 220 represents any type of mobilephone, and may include several computing resources, such as processor(s)810, RAM(S) 812 and ROM(S) 814, and one or more tangible storage devices818. SU 220, and may also include a read/write (R/W) interface 822, forexample, a USB port, to read from and write to external computingdevices or one or more portable computer-readable tangible storagedevices such as a CD-ROM, DVD, memory stick, magnetic disk, optical diskor semiconductor storage device. The apps and programs 832 and the userenvironment definitions 834 can be stored on the external computingdevices or one or more of the portable computer-readable tangiblestorage devices, read via the R/W interface 822 and loaded onto thecomputer-readable tangible storage device 818. Mobile phone SU 220 mayfurther include network and communication adapters or interfaces 820.These interfaces and adapters may allow communication over variousnetworks and protocols, for example, TDMA, CDMA, GSM, and/or othermobile telephone standards, WiFi, Ethernet, Bluetooth, NFC, and otherinfrastructure based and ad hoc wireless protocols.

As depicted in FIG. 6, Server 230 may include one or more processors902, one or more computer-readable RAMs 904, one or morecomputer-readable ROMs 906, one or more computer readable storage media908, device drivers 912, read/write drive or interface 914, networkadapter or interface 916, all interconnected over a communicationsfabric 918. The network adapter 916 communicates with a network 930.Communications fabric 918 may be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system.

One or more operating systems 910, and one or more application programs911, for example, machine operation monitoring system 200, as depictedin FIG. 2, are stored on one or more of the computer readable storagemedia 908 for execution by one or more of the processors 902 via one ormore of the respective RAMs 904 (which typically include cache memory).In the illustrated embodiment, each of the computer readable storagemedia 908 may be a magnetic disk storage device of an internal harddrive, CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, opticaldisk, a semiconductor storage device such as RAM, ROM, EPROM, flashmemory or any other computer-readable tangible storage device that canstore a computer program and digital information.

Server 230 may also include a R/W drive or interface 914 to read fromand write to one or more portable computer readable storage media 926.Application programs 911 on server 230 may be stored on one or more ofthe portable computer readable storage media 926, read via therespective R/W drive or interface 914 and loaded into the respectivecomputer readable storage media 908. Server 230 may also include anetwork adapter or interface 916, such as a Transmission ControlProtocol (TCP)/Internet Protocol (IP) adapter card or wirelesscommunication adapter (such as a 4G wireless communication adapter usingOrthogonal Frequency Division Multiple Access (OFDMA) technology).Application programs 911 on server 230 may be downloaded to thecomputing device from an external computer or external storage devicevia a network (for example, the Internet, a local area network or otherwide area network or wireless network) and network adapter or interface916. From the network adapter or interface 916, the programs may beloaded onto computer readable storage media 908. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers. Server 230may also include a display screen 920, a keyboard or keypad 922, and acomputer mouse or touchpad 924. Device drivers 912 interface to displayscreen 920 for imaging, to keyboard or keypad 922, to computer mouse ortouchpad 924, and/or to display screen 920 for pressure sensing ofalphanumeric character entry and user selections. The device drivers912, R/W drive or interface 914 and network adapter or interface 916 maycomprise hardware and software (stored on computer readable storagemedia 908 and/or ROM 906).

Server 230 can be a standalone network server, or representfunctionality integrated into one or more network systems. In general,server 230 can be a laptop computer, desktop computer, specializedcomputer server, or any other computer system known in the art. Incertain embodiments, server 230 represents computer systems utilizingclustered computers and components to act as a single pool of seamlessresources when accessed through a network, such as a LAN, WAN, or acombination of the two. This implementation may be preferred for datacenters and for cloud computing applications. In general, server 230 canbe any programmable electronic device, or can be any combination of suchdevices.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 7 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and machine operation monitoring 96.

Machine operation monitoring 96 may include functionality enabling thecloud computing environment to be used to receive and analyze signaldata as from machine-emitted signals of machines being monitored, and tosubsequently determine conditions of each of the machines beingmonitored. Machine operation monitoring 96 may also enable the cloudcomputing environment to be used to determine and effect correctiveaction on any particular machine, whose condition has been determined torequire such corrective action.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the one or more embodiment, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for monitoring a plurality of machineslocated in an operating environment, the method comprising: receiving,by a computing system, a first acoustic signal readings and theirrespective detecting locations from a sensing device in the operatingenvironment; generating, by the computing system, a first acousticsignal spatialization map containing characteristic data signatures,based on the first acoustic signal readings and their respectivedetecting locations, each of the characteristic data signatures beingassociated with one or more of the plurality of machines; receiving, bythe computing system, a second acoustic signal readings and theirrespective detecting locations from the sensing device in the operatingenvironment; generating, by the computing system, a second acousticsignal spatialization map containing characteristic data signatures,based on the second acoustic signal readings and their respectivedetecting locations, each of the characteristic data signatures beingassociated with one or more of the plurality of machines; determining,by the computing system, that there are one or more differences thatexceed a predetermined threshold value, between one or morecharacteristic data signatures in the first acoustic signalspatialization map and corresponding one or more characteristic datasignatures in the second acoustic signal spatialization map;identifying, by the computing system, at least one of the plurality ofmachines that are associated with the determined differences;identifying, by the computing system, a corrective action to perform ona machine of the plurality of machines, based on the determined one ormore differences in the generated first and second acoustic signalspatialization maps; transmitting, by the computing system, to acorrective action module in the operating environment, commands to causethe corrective action module to execute and perform the identifiedcorrective action on the machine of the plurality of machines, thecorrective action module comprising a cantilevered robotic arm rotatablyand pivotally attached to the corrective action module at a first end ofthe cantilevered robotic arm, and an end effector attached to a secondend of the cantilevered robotic arm, wherein the cantilevered roboticarm is usable to perform the identified corrective action.